Healthcare is extremely complex due to disparate systems and distributed data and the need to be efficient and adaptable. The mission of integrating decision support within health care applications is becoming even more challenging and daunting in a distributed environment due to further disparate data sources and ever changing requirements and expectations. For example, patient scheduling is complex due to large numbers of tasks that need to be completed by multiple departments distributed throughout. Coordination by wide number of care givers with different skill sets and functions is problematic.
Inpatients, outpatients, nurses, physicians, med techs, managers and others are distributed throughout the healthcare process. Shifts in health care towards shared patient-provider decision making and managed care add further complexity. Patient records are distributed across multiple locations, in various digital and physical formats, and the coordination of activities to be performed for health care in community care is increasingly complex. Moreover, there are increasing demands and needs to reduce errors in diagnosis and treatment, to provide health care to distributed locations, and to provide and promote better methods for education and training.
There are also increasing demands to keep up with new medicine, complex treatments and techniques, to find relevant data faster and simpler to assist care givers. There is an increasing need to control usage of restricted medicine and treatments, to be responsive to changes in environment and missing and incomplete data, and to provide better monitoring status of patient and co-operative decision making with the care giver. There is also a need to facilitate decision-making where uncertainty exists in diagnosis, therapy, drug prescription and testing.
Rule engines may be used for solving some of these problems when only simple scenarios exist or using only limited information. But rules engines become unmanageable when a rules library gets excessively large due to overlapping rules, contradictory rules, difficulties in rule verification and overall view of the active rules and support for multiple disparate rule engines. Such systems can result in less-than-optimized solutions, conflicting orders, or are simply unable to handle more complex scenarios such as patients having multiple conditions, when conditions are interdependent.